Programme leadership • Curriculum design • Data science education
As Programme Leader for multiple BSc Data Science programmes at Bournemouth University, I oversee curriculum development, quality assurance, student experience, and industry engagement for undergraduate data science education. My leadership ensures programmes remain aligned with industry needs and equip graduates with competitive technical and professional skills.
Leading a comprehensive undergraduate programme combining statistical foundations, machine learning, data engineering, and business analytics. Emphasis on hands-on project-based learning, industry placement opportunities, and real-world data challenges.
Extended programme providing a foundation year for students transitioning into data science from non-traditional backgrounds. Building quantitative skills, programming competencies, and study skills before progressing to full BSc curriculum.
Four-year programme incorporating a full-year industry placement. Students gain professional experience, apply classroom learning in real-world settings, and build industry networks before graduation. Strong employer engagement and placement support.
Design and refresh programme content to reflect evolving industry practices, emerging technologies (LLMs, MLOps, cloud AI), and employer demand for technical skills
Ensure programme compliance with professional body standards, university regulations, and QAA Subject Benchmarks. Monitor student outcomes and drive continuous improvement
Oversee pastoral support, academic advising, and student engagement initiatives. Champion diversity, inclusion, and widening participation in data science education
Build partnerships with employers for guest lectures, placement opportunities, capstone projects, and curriculum review. Advisory board coordination
Throughout my academic career, I have taught extensively across mathematics, statistics, econometrics, data science, and machine learning. My teaching philosophy emphasises rigorous quantitative foundations, practical computational skills, and real-world application.
Level: Undergraduate & Postgraduate
Core module covering supervised and unsupervised learning, neural networks, deep learning architectures (CNNs, RNNs, Transformers), model evaluation, and ethical AI considerations. Hands-on implementation using Python (scikit-learn, TensorFlow, PyTorch).
Level: Undergraduate
Introduction to data science pipeline: data acquisition, cleaning, exploratory analysis, visualisation, and communication. Focus on Python (pandas, matplotlib, seaborn) and reproducible workflows.
Level: Undergraduate & Postgraduate
Statistical inference, hypothesis testing, regression analysis, time-series methods, and causal inference. Applications in economic analysis, policy evaluation, and business analytics using R and Stata.
Level: Postgraduate
Applied analytics for managerial decision-making. Topics include predictive modelling, optimisation, simulation, prescriptive analytics, and dashboard design. Industry case studies and consulting projects.
Level: Undergraduate (Foundation & Year 1)
Mathematical foundations: linear algebra, calculus, probability theory, and optimisation. Building quantitative skills for advanced data science and machine learning study.
My teaching approach combines rigorous theoretical foundations with hands-on practical application. I believe effective data science education requires: